pseudonimização

Pseudonimização is a data processing technique that replaces direct identifiers in a dataset with pseudonyms. These pseudonyms are artificial identifiers, such as numbers or random strings, that do not reveal the identity of the data subject on their own. The original data is kept separate from the pseudonymous data, and the link between them is maintained through a secure key or mapping table. This process is a key measure for protecting personal data privacy, particularly within the framework of regulations like the General Data Protection Regulation (GDPR).

The primary purpose of pseudonimização is to reduce the risk of re-identification. By removing direct identifiers, it becomes significantly harder for unauthorized individuals to link the data back to a specific person. This makes the data less sensitive and can allow for broader use in research, analysis, or testing while still respecting privacy principles. However, it's important to note that pseudonimization is not anonymization. If the pseudonymizing key is compromised or the context of the data allows for re-identification, the data can still potentially be linked back to the individual.

Pseudonimização is a flexible technique that can be applied in various ways. For instance, a customer's name might be replaced with a unique customer ID. The effectiveness of pseudonimização depends on the quality of the pseudonymization process and the security measures in place to protect the link between the pseudonyms and the original identifiers. It is a recommended security measure for many types of data processing where the direct identification of individuals is not strictly necessary.